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Author
M. Bosma
E. Meij
W. Weerkamp
Year
2012
Title
A framework for unsupervised spam detection in social networking sites
Journal
Lecture Notes in Computer Science
Volume
7224
Pages (from-to)
364-375
Document type
Article
Faculty
Faculty of Science (FNWI)
Institute
Informatics Institute (IVI)
Abstract
Social networking sites offer users the option to submit user spam reports for a given message, indicating this message is inappropriate. In this paper we present a framework that uses these user spam reports for spam detection. The framework is based on the HITS web link analysis framework and is instantiated in three models. The models subsequently introduce propagation between messages reported by the same user, messages authored by the same user, and messages with similar content. Each of the models can also be converted to a simple semi-supervised scheme. We test our models on data from a popular social network and compare the models to two baselines, based on message content and raw report counts. We find that our models outperform both baselines and that each of the additions (reporters, authors, and similar messages) further improves the performance of the framework.
URL
go to publisher's site
Language
English
Note
Proceedings title: Advances in information retrieval: 34th European Conference on IR Research, ECIR 2012, Barcelona, Spain, April 1-5, 2012. Proceedings
Publisher: Springer
Place of publication: Berlin
ISBN: 978-3-642-28996-5
Editors: R/ Baeza-Yates, A.P. de Vries, H. Zaragoza, B.B. Cambazoglu, V. Murdock, R. Lempel, F. Silvestri
Permalink
http://hdl.handle.net/11245/1.381118

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